class_docstr: Args:
      input_shape (int): network input image size
      ori_shape (int): ori image shape of before padding
      scale_factor (float): scale factor of ori image
      enable_mkldnn (bool): whether to open MKLDNN
class_docstr_tok: ["Args",":","input_shape","(","int",")",":","network","input","image","size","ori_shape","(","int",")",":","ori","image","shape","of","before","padding","scale_factor","(","float",")",":","scale","factor","of","ori","image","enable_mkldnn","(","bool",")",":","whether","to","open","MKLDNN"]
code: def __init__(
      self,
      input_shape,
      ori_shape,
      scale_factor,
      strides=[8, 16, 32, 64],
      score_threshold=0.4,
      nms_threshold=0.5,
      nms_top_k=1000,
      keep_top_k=100,
  ):
      self.ori_shape = ori_shape
      self.input_shape = input_shape
      self.scale_factor = scale_factor
      self.strides = strides
      self.score_threshold = score_threshold
      self.nms_threshold = nms_threshold
      self.nms_top_k = nms_top_k
      self.keep_top_k = keep_top_k
  
file_path: PaddleDetection/deploy/third_engine/demo_openvino/python/openvino_benchmark.py
imports: import cv2
  import numpy
  import time
  import argparse
  from scipy.special import softmax
  from openvino.runtime import Core
label_desc: Use image node_id 1 to create a new PicoDetPostProcess object from inherited base classes: object with example: obj = PicoDetPostProcess(input_shape, ori_shape, scale_factor, strides, score_threshold, nms_threshold, nms_top_k, keep_top_k)